A number of systems and programs are offered on the market for the design, the engineering and the manufacturing of objects. CAD is an acronym for Computer-Aided Design, e.g. it relates to software solutions for designing an object. CAE is an acronym for Computer-Aided Engineering, e.g. it relates to software solutions for simulating the physical behaviour of a future product. CAM is an acronym for Computer-Aided Manufacturing, e.g. it relates to software solutions for defining manufacturing processes and operations. In such computer-aided design systems, the graphical user interface plays an important role as regards the efficiency of the technique. These techniques may be embedded within Product Lifecycle Management (PLM) systems. PLM refers to a business strategy that helps companies to share product data, apply common processes, and leverage corporate knowledge for the development of products from conception to the end of their life, across the concept of extended enterprise. The PLM solutions provided by Dassault Systèmes (under the trademarks CATIA, ENOVIA and DELMIA) provide an Engineering Hub, which organizes product engineering knowledge, a Manufacturing Hub, which manages manufacturing engineering knowledge, and an Enterprise Hub which enables enterprise integrations and connections into both the Engineering and Manufacturing Hubs. All together the system delivers an open object model linking products, processes, resources to enable dynamic, knowledge-based product creation and decision support that drives optimized product definition, manufacturing preparation, production and service.
CAD/CAE systems are used to enable simulation of a physical system that can belong but is not limited to aeronautics, automobile, marine, and civil engineering. Notably, these systems are used for performing simulations of physical phenomena on physical system by the Finite Element Method (FEM). Such simulations allow predicting the performance (thermal, mechanical or any other physical performance) of a physical system in service, as well as optimizing the design to improve said performance. They also allow predicting the manufacturability of the part. Manufacturability can be at stake in the case of 3D printing, e.g. simulating the print process with phenomena such as heat distribution in the part during direct metal printing. Indeed, phenomena such as residual stresses can be highly problematical in 3D printing, and can prevent a physical system from being certified for use in critical applications such as aeronautics. Being able to simulate phenomena that occur during the print process is thus highly important.
Lattice structures are often used for designing one or more parts a modeled physical system. A lattice structure denotes a repetitive alternating solid and void pattern in a part that models a physical system. The purpose of this lattice pattern can be to tailor the weight, mechanical resistance, heat transfer or other properties of the part. Thus, lattice structures improve the global performances of a part; notably, mechanical stresses can be better distributed through the part thank to the lattice structure. The design and analysis of Lattice Solids has become very important due to increased interest in 3D Printing. 3D printers do not impose almost any limitations on the complexity of a shape. Therefore, designers are free to create very complex shapes which were not previously manufacturable. In particular, 3D Printers can easily print lattice solids.
In order to analyze a modeled physical system by Finite Element Analysis (FEA), it must be decomposed into a multitude of Finite Elements. The process of decomposing a part of the modeled physical system into elements is called meshing. The finer this decomposition, the more precise the finite element analysis will be. It is not uncommon today to see a single mechanical part decomposed into several million elements. In the beginning of Finite Element Analysis, the layout of the mesh was created by hand, but this was a painful process that was a major bottleneck for realizing the true benefit of physics simulation, as every new or Improved design involved the task of re-creating this finite element mesh. In the last 20 years, automated 3D meshing has come to replace manual meshing, making the overall cost of the Finite Element Analysis much lower. Two major element shapes are dominant in Finite Element Meshing: the Tetrahedron (Tet), with 4 sides, and the Hexahedron (Hex), with 6 sides. For most types of problems, Finite Element Analysis will be more efficient on Hex elements. In consequence, when meshing by hand, the engineer would Introduce a large proportion of these hexahedral elements. However, 3D hex meshing is has proved very difficult to automate. As a result most automated 3D meshing in use today is Tetrahedra based. The sacrifice in computation efficiency is acceptable for users because of the immense gain in overall productivity introduced by automated 3D meshing.
Automated 3D meshing, in general, attempts to fill a volume with finite elements without making too many assumptions about the nature of the volume. Because of all the potential shape variation of the volumes to be filled, the algorithms are necessarily complex and costly in execution time. The most important factor in the execution time of these algorithms is the boundary complexity of the shape to fill.
Automated 3D meshing suffers problems in the case of lattice structures. A first problem is that lattice solids are micro-porous, like a sponge. This means they have particularly high boundary complexity and are very poorly suited to automated 3D meshing which scales in complexity with the boundary. For lattices with tens or hundreds of thousands of unit cells, the boundary becomes too complex, making the use of automated 3D meshing impractical. Another problem is that automated 3D meshing does not produce an identical mesh in each lattice cell, making for uneven results and local artefacts, which create noise and imprecision in the analysis. To avoid such artefacts, the user might choose to generate a finer mesh, but this constitutes a vicious circle as it will aggravate the scaling problem of the first problem mentioned hereinabove. Furthermore, 3D meshing will not take advantage of the repetitive nature of the lattice solid in order to choose element types that are conducive to most efficient computation. In particular, automated 3D meshing will typically fill the solid with tetrahedral elements, whereas the technology of Finite Element Analysis will (in most cases) perform best on a hexahedral-dominant mesh—that is a mesh where the majority of elements are hexahedrons-.
Within this context, there is still a need for an improved design of a 3D structure of 3D finite element mesh of a 3D part that comprises a lattice structure.